Airborne　pollen　identification　is　crucial　to　help　patients　prevent　pollinosis　symptoms.　Existing　data-driven　methods　rely　on　large-scale　pollen　images　with　simple　backgrounds.　In　real　scenarios,　the　background　is　complex　and　the　data　scale　is　small.　Therefore,　these　methods　suffer　from　two　challenges:　(1)　Irrelevant　information　interference;　(2)　Incomplete　feature　attention.　To　overcome　these　challenges,　we　propose　a　prior　knowledge-guided　deep　feature　learning　(PK-DFL)　for　real-world　optical　microscope　image　classification.　Its　main　steps　are　as　follows:　Pollen　location　is　designed　to　locate　pollen　grains　based　on　color　features,　aiming　to　boost　the　accuracy　of　shape　and　texture　prior　feature　extraction.　Shape-texture　awareness　helps　to　extract　the　shape　and　texture　of　pollen　grains　via　predefined　feature　extractors　(i.e.,　a　set　of　shape　descriptors　and　an　improved　SFTA).　These　features　are　used　to　construct　two　types　of　prior　knowledge,　namely　shape-texture　attention　maps　(STA　maps)　and　shape-texture　feature　vectors　(STF　vectors).　Pollen　classification　uses　a　deep　network　(CNN)　to　classify　pollen　via　imitating　the　pollen　identification　procedure　of　palynologists.　It　uses　STA　maps　to　weight　pollen　images　and　convolutional　feature　maps　for　instructing　the　CNN　to　focus　on　critical　areas　of　pollen　images　(　for　the　first　challenge).　).　STF　vectors　are　employed　to　obtain　the　inter-class　similarity　of　pollen　via　template　matching.　This　information　is　further　converted　to　soft　targets　that　are　used　to　supervise　the　CNN　attending　to　comprehensive　key　features　(　for　the　second　challenge).　).　Extensive　experiments　on　real-world　datasets　demonstrate　the　effectiveness　of　our　PK-DFL　(with　accuracy　and　F1-score　over　88%).